Browsing by Author "Ochen, William"

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Advanced graphene–MXene–black phosphorus multilayered metasurface sensor for high-sensitivity terahertz brain tumor detection
(AIP Advances, 2026-03-11) Wekalao, Jacob; Elsayed, Hussein A.; Alqhtani, Haifa A.; Almawgani, Abdulkarem H. M; Gumaih, Hussein S.; Adam, Yousif S.; Mehaney, Ahmed; Ochen, William
In this research, we present a multilayer metasurface sensor design integrating graphene, MXene, black phosphorus, and gold for the ultrasensitive detection of brain tumor biomarkers in liquid biopsy samples. The hierarchical structure consists of a MXene-coated rectangular resonator, a black phosphorus-coated square resonator, a gold-coated circular ring, and a graphene-based circular substrate. This architecture was systematically optimized through comprehensive numerical simulations using COMSOL Multiphysics 6.3, integrated with machine learning frameworks. The proposed sensor demonstrates an outstanding sensitivity of 2308 GHz/RIU across a physiologically relevant refractive index range (1.3333–1.4833), significantly outperforming current state-of-the-art devices. Performance analysis identifies an optimal sensing regime at RI = 1.3425, achieving a figure of merit of 20.79 RIU−1 and a detection limit as low as 0.079 RIU. Detailed investigations of the transmission spectra under varying graphene chemical potentials (0.1–0.9 eV), incident angles (0○ –80○ ), and geometric modifications of the resonators reveal highly tunable sensing behavior. Furthermore, Random Forest Regression models achieve predictive accuracies of 85%–100%, enabling reliable estimation of sensor performance across diverse operating conditions. Collectively, these results establish a solid foundation for employing advanced 2D material–based metasurfaces in minimally invasive and early-stage brain tumor diagnostics, thereby advancing the capabilities of next-generation liquid biopsy technologies.
Design and optimization of a hybrid graphene–gold–silver terahertz metasurface biosensor for high-sensitivity sperm detection with machine learning for behavior prediction
(Journal of Electronic Materials, 2025-11-25) Muheki, Jonas; Elsayed, Hussein A.; Alfassam, Haifa E.; Ochen, William; Rajakannu, Amuthakkannan; Mehaney, Ahmed; Wekalao, Jacob
This study introduces a plasmonic-based sensor for sperm detection, integrating gold, graphene, and black phosphorus within a tailored multilayer structure. The sensor design consists of a silver-coated circular ring resonator (radius: 2–2.5 µm), a black phosphorus-coated square ring (7–8 µm), and four gold-coated circular resonators (each with a 2 µm radius) placed on a graphene-coated square platform. Electromagnetic simulations performed using COMSOL Multiphysics indicate optimal sensing performance within the 0.1–0.6 THz frequency range. The sensor demonstrates remarkable sensitivity of 5000 GHz per refractive index unit (RIU−1), a figure of merit of 90.909 RIU−1, and a detection limit of 0.02 RIU. It is capable of detecting sperm concentrations in a range of 17–197 million/mL, corresponding to refractive index variations from 1.33 to 1.3461. Furthermore, performance optimization through XGBoost machine learning achieved perfect prediction accuracy (R2 = 1.00) across all test cases. This high-efficiency sensor marks a significant step forward in sperm detection technologies, with promising applications in male fertility assessment and reproductive medicine
High-sensitivity terahertz metasurface biosensor for multi-cancer detection: a machine learningenhanced approach using graphene–MXene– silver–copper hybrid architecture
(Materials Technology Advanced Performance Materials, 2025-12-19) Wekalao, Jacob; Elsayed, Hussein A.; Mehaney, Ahmed; Ochen, William; Othman, Sarah I.; Bellucc, Stefano; Amuthakkannan, Rajakannu; Ahmed, Ashour M.; Muheki, Jonas
Early cancer detection requires highly sensitive diagnostic tools beyond the capabilities of conventional imaging and biopsy methods. We present a terahertz (THz) metasurface biosensor that integrates a copper-coated H-shaped resonator with three silver rectangular resonators enclosed within an MXene circular ring. The design incorporates complex electromagnetic interactions, nonlocal effects, and coupled-mode modelling to optimise performance. The biosensor achieves a sensitivity of 1000 GHz/RIU, a quality factor of 3.6–3.747, and a figure of merit up to 13.333 RIU⁻¹. It maintains stable absorption (52.789–53.804%) across 0.27–0.281 THz, with a linear resonance–refractive-index response (R² = 0.95276). Machine-learning optimisation of graphene chemical potential further enhances predictive accuracy (R² = 0.93). By enabling simultaneous detection of multiple cancer biomarkers through frequency-shift analysis, this noninvasive platform offers strong potential for real-time, early-stage cancer screening.
Machine-learning-assisted multilayer graphene–silver–ZrN surface plasmon resonance biosensor for high-sensitivity hemoglobin detection
(Materials Technology, 2026-02-09) Ochen, William; Wekalao, Jacob; Muheki, Jonas; Elsayed, Hussein A.; Alqhtani, Haifa A.; Almawgani, Abdulkarem H. M.; Alhawari, Adam R.; Mehaney, Ahmed; Solouma, Emad
This work presents a theoretically optimized multilayer surface plasmon resonance (SPR) biosensor for quantitative hemoglobin detection using the Kretschmann configuration. The sensor integrates a BK-7 prism, silver plasmonic layer, graphene enhancement layer, zirconium nitride (ZrN) protective layer, and aqueous sensing medium. This architecture synergistically combines enhanced electromagnetic confinement with chemical stability, addressing silver's oxidation vulnerability while maintaining superior plasmonic performance. Electromagnetic analysis via transfer matrix method and finite element simulations demonstrates exceptional sensitivity metrics: maximum angular sensitivity of 500°/RIU, figure of merit of 92.25 RIU⁻¹, and detection limit of 0.006 RIU across clinically relevant hemoglobin concentrations (10–40 g/L). Localized electric field enhancement (~10⁶ V/m) at the sensing interface confirms optimal light-matter interaction amplification. Machine learning models predict sensor responses to graphene thickness and refractive index variations with R² > 0.99, enabling rapid optimization. This design advances SPR biosensor technology for sensitive, label-free biochemical detection applications.
Mechanical properties of ceramic floor tiles made from selected minerals in Uganda
(Kyambogo University [unpublished work], 2012-06) Ochen, William
This study was designed to produce porcelain floor tiles using raw materials found in Uganda and to determine mechanical properties of the tiles produced. The properties were then compared with South African National Standards. Porcelains are made of clays, kaolin, feldspar and sand. In this study, the raw materials were mixed in proportions of 40-60% clays, 30-40% feldspar and 10-30% sand. The tests carried out include shrinkage, strength, water absorption and chemical analysis. Samples were pressed at 30MPa with 12% moisture content by weight. Firing was at peak temperatures of 1050, 1100, 1150, 1200 and 12500C. Firing process involved heating dry samples from room temperature to 1050C at 1000C/hr, after 2 hrs of holding the rate was then raised to 3600 C/hr and soaked for 1 hr at peak temperatures. Samples were left to cool naturally in the kiln for 16 hrs. Chemical analysis of the minerals by XRF method revealed that kaolin had an alumina content of 24.9 (wt %) whereas sand had silica at 95.0 (wt %). Iron oxide which influences the color of samples was high in ball clay at 3.2 (wt %). Maximum strength of 34MPa was exhibited by composition 2 at 12500C with linear shrinkage of 9.1 % and water absorption of 0.0 (wt %). South African National Standards classifies tiles whose strength is above 30MPa as B1. All samples fired in the temperature range of 1100 to 12500C exhibited properties in the range recommended by SANS both in strength and water absorption for flooring as B1 to B4 tiles. Minerals from the studied deposits yielded high grade ceramic floor tiles recommended by SANS for flooring. No cracks were exhibited by samples fired at 1100 to 12500C
Multi-resonator plasmonic metasurface biosensor with graphene enhancement for ultra-sensitive terahertz pregnancy detection using machine learning optimization
(Journal of Electromagnetic Waves and Applications, 2025-11-27) Wekalao, Jacob; Muhek,Jonas; Elsayed, Hussein A.; Mehaney,Ahmed; Othmane, Sarah I.; Abukhadra, Mostafa R.; Bellucci, Stefano; Rajakannu,Amuthakkannan; Ochen, William
This study presents a multi-resonator plasmonic metasurface biosensor operating in the terahertz range for detecting human chorionic gonadotropin (hCG), a primary pregnancy biomarker. The sensor consists of four resonators with different geometries and dimensions made from graphene, copper, aluminum, and gold. Its operation is based on surface plasmon resonance. Finite element simulations showed that transmittance varied from 98.428% to 30.736% as the graphene chemical potential changed from 0.1 to 0.45 eV. The optimized sensor achieved a sensitivity of 1000 GHz per refractive index unit (RIU) and a figure of merit of 13.333 RIU−1 . A Gradient Boosting Regressor model was used to predict sensor behavior. The model produced R 2 values between 0.90 and 1.00 for variations in incident angle, square ring geometry, and graphene chemical potential. Resonance frequency shifted from 0.32 to 0.30 THz with refractive index changes, following a linear relationship (R2 = 0.88947) that allows calibration for hCG detection.